A convolutional neural network segments yeast microscopy images with high accuracy

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

Fine-grained classification of fly species in the natural environment based on deep convolutional neural network

Effective classification of flies is beneficial to prevent the spread of disease and protect agricultural production. It is important to prevent the invasion of fly species. Aiming at the problem of similar morphology and difficulty in the classification of fly species in the natural environment, this paper proposes a fine-grained classification method for fly species in the complex natural environment based on deep convolutional neural network. Firstly, the specific position of the fly in the image is located by the gradient-weighted class activation graph method, and the object region of the fly is obtained. Then, the local region containing the most abundant information in the image is extracted. When extracting features from the local region, the attention module and cross-layer bilinear pooling are combined. The feature information of different convolutional layers is integrated. Finally, the global and local feature information is integrated for classification. We experimentally compared the proposed method with other state-of-the-art methods on the established dataset. Experimental results show that the accuracy of the proposed method on the three datasets is 84.34%, 89.53% and 93.26%, respectively. Compared with other state-of-the-art methods, this method has a good classification effect on fly species.

YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy

Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub.